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Cited by 200Metal-Organic Frameworks: Synthesis and ApplicationsMachine Learning in Materials ScienceCovalent Organic Framework Applications

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks

Open access
Nandy, Aditya, Duan, Chenru, Kulik, Heather J.·2021·Journal of the American Chemical Society
DOI 10.1021/jacs.1c07217
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Abstract

Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of understanding of how to improve their stability limits their use in practice. To overcome this limitation, we extract thousands of published reports of the key aspects of MOF stability necessary for their practical application: the ability to withstand high temperatures without degrading and the capacity to be activated by removal of solvent molecules. From nearly 4000 manuscripts, we use natural language processing and image analysis to obtain over 2000 solvent-removal stability measures and 3000 thermal degradation temperatures. We analyze the relationships between stability properties and the chemical and geometric structures in this set to identify limits of prior heuristics derived from smaller sets of MOFs. By training predictive machine learning (ML, i.e., Gaussian process and artificial neural network) models to encode the structure-property relationships with graph- and pore-structure-based representations, we are able to make predictions of stability orders of magnitude faster than conventional physics-based modeling or experiment. Interpretation of important features in ML models provides insights that we use to identify strategies to engineer increased stability into typically unstable 3d-transition-metal-containing MOFs that are frequently targeted for catalytic applications. We expect our approach to accelerate the time to discovery of stable, practical MOF materials for a wide range of applications.

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Crossref · 10.1021/jacs.1c07217

Cites

88 of 88 references verified0 citation errors · 0 not checkable

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  • In cases where no physics-based modeling protocol is established, ML can be used to bring computational materials science closer to counterpart experimental efforts, such as by predicting quantities related to synthesis or materials stability.

    “in cases where no physics-based modeling protocol is established, ML can be used to bring computational materials science closer to counterpart experimental efforts, such as by predicting quantities related to synthesis (12)(13)(14)(15)(16) or materials stability (17).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [17]

  • Nandy et al. showed it was possible to redesign linker chemistry to enhance stability in known materials, suggesting a strategy for reengineering materials with improved properties.

    “Through these models, Nandy et al. (17) showed it was possible to redesign linker chemistry (e.g., functional groups) to enhance stability in known materials, suggesting a strategy for reengineering materials with improved properties.”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [17]

  • In experimental data sets extracted from the literature, there can be positive bias for reporting only successful experiments, leading to challenges in classification tasks.

    “For example, in experimental data sets extracted from the literature (14,15,19), there can be positive bias for reporting only successful experiments (16, 107, 108), leading to challenges in classification tasks (17).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [17]

  • Large data sets are seldom available for new classes of materials, owing to either a lack of prior experimentation or inconsistent reporting in the scientific literature.

    “Although careful extraction of experimental literature data (17) or high-throughput experimentation can address some of these challenges, large data sets are seldom available for new classes of materials, owing to either a lack of prior experimentation or inconsistent reporting in the scientific literature.”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [17]

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Research works printed but not linked: 2

  1. [1]1. Allen, F. H. Acta Crystallographica Section B: Structural Science 2002, 58 , 380-388.
  2. [2]2. Wang, F.; Tan, Y.-X.; Zhang, J. Solid State Sci. 2012, 14 , 1263-1266.

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Authors
Nandy, A., Duan, C., & Kulik, H. J.
Affiliations
Massachusetts Institute of Technology (US)
ORCID
Venue
Journal of the American Chemical Society
Publisher
American Chemical Society (ACS)
Published
2021-10-13
Volume
143
Issue
42
Pages
17535-17547
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
DOI
10.1021/jacs.1c07217
OpenAlex
W3175436502
Cited by
200
References
88
Citations by year
2026: 34 · 2025: 48 · 2024: 51 · 2023: 42 · 2022: 23 · 2021: 2
FWCI
9.53
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of the American Chemical Society (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Defense Advanced Research Projects Agency (D18AP00039)
  • American Association for the Advancement of Science
  • Basic Energy Sciences (DE-SC0012702)
  • Division of Graduate Education (1122374)
  • Office of Naval Research (N00014-20-1-2150)
  • Burroughs Wellcome Fund
Topics
  • Physical Sciences › Chemistry › Inorganic Chemistry › Metal-Organic Frameworks: Synthesis and Applications
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Materials Science › Materials Chemistry › Covalent Organic Framework Applications
Keywords
metal-organic frameworks, MOF stability, thermal degradation, structure-property relationships, graph neural networks, pore structure, catalysis, gas separation

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